import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import matplotlib


# 假定学习时长是 1 2 3小时
x_data = torch.Tensor([[1.0],
                       [2.0],
                       [3.0]])

# 0 表示未通过考试，1表示通过考试
y_data = torch.Tensor([[0],
                       [0],
                       [1]])

class LogisticRegressionModel(torch.nn.Module):
    def __init__(self):
        super(LogisticRegressionModel, self).__init__()
        self.linear = torch.nn.Linear(1, 1)

    def forward(self, x):
        y_pred = F.sigmoid(self.linear(x))
        return y_pred

model = LogisticRegressionModel()
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)

for epoch in range(100):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

x = np.linspace(0, 10, 200)
x_t = torch.Tensor(x).view((200, 1))
y_t = model(x_t)
y = y_t.data.numpy()

matplotlib.use("TkAgg")
plt.plot(x, y)
plt.plot([0, 10], [0.5, 0.5], c = 'r')
plt.xlabel("Hours")
plt.ylabel("Probability of Pass")
plt.grid()
plt.show()



